237
50 %
14000-79500
Created by: Mercy
Dates :
---
title: "Data Analysis Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "twitter", "facebook", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(modelr)
library(broom)
library(caret)
library(rpart)
library(ggplot2)
library(Amelia)
library(dplyr)
library(data.table)
library(tidyverse)
```
```{r}
crypto_advertisement <- read.csv('~/R/R dashboards/advertising.csv')
```
Data Visualization
=====================================
## Row 1 {data-height=110}
### Number of countries
```{r}
#unique(crypto_advertisement$Country)
valueBox(237, icon = "fa-ship", color="rgb(100,100,100)")
```
### Percentage of Clicks on Ad
```{r}
#table(crypto_advertisement$Clicked.on.Ad)
valueBox("50 %", icon = "fa-heart", color="rgb(200,100,100)")
```
### Income Range for users
```{r}
#max(crypto_advertisement$Area.Income)
valueBox("14000-79500", icon = "fa-life-ring",color="rgb(26,110,204)")
```
## Row 2 {data-height=400}
### Age Distribution
```{r}
#install.packages('highcharter')
library(highcharter)
hc <- hchart(
density(crypto_advertisement$Age),
type = "area", name = "Users age"
)
hc
#hist(crypto_advertisement$Age, breaks=12, col="cyan3",xlab="Ages of Users", main='Age distribution of users')
```
### Internet Usage
```{r}
#hist(crypto_advertisement$Daily.Internet.Usage, breaks=12, col="cyan4",xlab="Daily Internet Usage", main='Daily Internet Usage distribution of users')
daily.internet.usage <- hchart(
crypto_advertisement$Daily.Internet.Usage,breaks=12,
color = "#008B8B", name = "Daily.Internet.Usage"
)
daily.internet.usage
```
More Visualizations
=====================================================
## Row 1
### Area Income compared to time spent on site
```{r}
#install.packages(plotly) # if you haven't installed the package
library(plotly)
m <- ggplot(data = crypto_advertisement, aes(x = Area.Income, y = Daily.Time.Spent.on.Site)) +
geom_point() + # then add a layer of points
geom_smooth(method = "lm") # and then add a fitted line
ggplotly(m)
```
### Daily.Internet.Usage compared to time spent on site
```{r}
cv <- ggplot(data = crypto_advertisement, aes(x = Daily.Internet.Usage, y = Daily.Time.Spent.on.Site)) +
geom_point() + # then add a layer of points
geom_smooth(method = "lm") # and then add a fitted line
ggplotly(cv)
```
Row
------------------------------------
### Gender Representations
```{r}
#changing column name
names(crypto_advertisement)[names(crypto_advertisement) == "Male"] <- "Gender"
#replacing gender values
crypto_advertisement <- within(crypto_advertisement, Gender[Gender == 0] <- 'Female')
crypto_advertisement <- within(crypto_advertisement, Gender[Gender == 1] <- 'Male')
pie(table(crypto_advertisement$Gender), main = "Gender Representations",
col = c("darkcyan", "darkgray"), radius = 1)
```
Report
========================================
Created by: Mercy
Dates :